Traj estimates a discrete mixture model for clustering of longitudinal data series.
Groups may represent distinct subpopulations
or alternatively, components of a discrete approximation
for a potentially complex data distribution.

Supported distributions are: censored (or
regular) normal, zero inflated (or regular) Poisson, and Bernoulli
distributions (logistic model). The censored normal model is useful for
psychometric scale data with censoring at a scale minimum and/or scale maximum, the zero inflated Poisson model useful for count data
with more zeros than would be expected under the Poisson assumption, and the Bernoulli model useful for 0/1 data. The model is
appropriate for data with average values changing smoothly as a function of the
dependent variable (time, age, ...). Some sharp changes can be handled through
the inclusion of time dependent covariates.